Expectation–maximization algorithm with a nonlinear kalman smoother for MEG/EEG connectivity estimation

Narayan Puthanmadam Subramaniyam*, Filip Tronarp, Simo Särkkä, Lauri Parkkonen

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

2 Sitaatiot (Scopus)

Abstrakti

Current techniques to determine functional or effective connectivity from magnetoencephalography (MEG) and electroencephalography (EEG) signals typically involve two sequential steps: 1) estimation of the source current distribution from the sensor data, for example, by minimum-norm estimation or beamforming, and 2) fitting a multivariate autoregressive (MVAR) model to estimate the AR coefficients, which reflect the interaction between the sources. Here, we introduce a combination of the expectation–maximization (EM) algorithm and a nonlinear Kalman smoother to perform joint estimation of both source and connectivity (linear and nonlinear) parameters from MEG/EEG signals. Based on simulations, we show that the proposed approach estimates both the source signals and AR coefficients in linear models significantly better than the traditional two-step approach when the signal-to-noise ratio (SNR) is low (≤1) and gives comparable results at higher SNRs (>1). Additionally, we show that nonlinear interaction parameters can be reliably estimated from MEG/EEG signals at low SNRs using the EM algorithm with sigma-point Kalman smoother.

AlkuperäiskieliEnglanti
OtsikkoEMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017
Sivut763-766
Sivumäärä4
DOI - pysyväislinkit
TilaJulkaistu - 2017
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaJoint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107 - Tampere, Suomi
Kesto: 11 kesäkuuta 201715 kesäkuuta 2017

Julkaisusarja

NimiIFMBE Proceedings
KustantajaSpringer-Verlag
Vuosikerta65
ISSN (painettu)1680-0737

Conference

ConferenceJoint Conference of the European Medical and Biological Engineering Conference, EMBEC 2017 and Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2107
MaaSuomi
KaupunkiTampere
Ajanjakso11/06/201715/06/2017

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  • Siteeraa tätä

    Subramaniyam, N. P., Tronarp, F., Särkkä, S., & Parkkonen, L. (2017). Expectation–maximization algorithm with a nonlinear kalman smoother for MEG/EEG connectivity estimation. teoksessa EMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017 (Sivut 763-766). (IFMBE Proceedings; Vuosikerta 65). https://doi.org/10.1007/978-981-10-5122-7_191